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<p>Basecalling is a crucial step in DNA sequencing that converts raw nanopore signals into nucleotide sequences. This paper presents a serial-parallel reprogrammable DNA sequencing accelerator based on a 64-state Hidden Markov Model (HMM) implemented in a 130-nm CMOS process. The proposed method optimizes computational efficiency, hardware utilization, and power consumption using a coarse-grained serial-parallel processing approach. It achieves 94.3% accuracy, outperforming Nanocall (85.6%) and Meta-Align (91.2%), while being slightly superior to the Scalable Hardware Accelerator (93.1%). Furthermore, it consumes 200 mW, which is 6 times lower than brute-force HMM implementations and 3–5 times more power-efficient than deep learning-based basecallers like DeepNano and Bonito. The proposed accelerator maintains competitive throughput at 8&#xa0;M Bases/sec, balancing processing speed and energy efficiency. Additionally, the architecture supports scalability up to 4096 states, making it highly adaptable for various sequencing applications. It’s hardware-optimized and low-power design makes it an ideal alternative to brute-force and software-based methods for real-time, mobile, and embedded DNA sequencing devices.</p>

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Low power reprogrammable DNA basecaller with an efficient HMM accelerator for real time nanopore sequencing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月28日

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  • Atefeh Salimi Shahraki,
  • Sebastian Magierowski,
  • Mahdi Abbasi,
  • Mehrdad Ahmadi Kamarposhti

摘要

Basecalling is a crucial step in DNA sequencing that converts raw nanopore signals into nucleotide sequences. This paper presents a serial-parallel reprogrammable DNA sequencing accelerator based on a 64-state Hidden Markov Model (HMM) implemented in a 130-nm CMOS process. The proposed method optimizes computational efficiency, hardware utilization, and power consumption using a coarse-grained serial-parallel processing approach. It achieves 94.3% accuracy, outperforming Nanocall (85.6%) and Meta-Align (91.2%), while being slightly superior to the Scalable Hardware Accelerator (93.1%). Furthermore, it consumes 200 mW, which is 6 times lower than brute-force HMM implementations and 3–5 times more power-efficient than deep learning-based basecallers like DeepNano and Bonito. The proposed accelerator maintains competitive throughput at 8 M Bases/sec, balancing processing speed and energy efficiency. Additionally, the architecture supports scalability up to 4096 states, making it highly adaptable for various sequencing applications. It’s hardware-optimized and low-power design makes it an ideal alternative to brute-force and software-based methods for real-time, mobile, and embedded DNA sequencing devices.